Use AI inside bounded workflows with review, fallback, and evidence built in.

Generated output becomes operational risk when the source data, allowed decisions, review owner, confidence threshold, validation checks, exception path, and fallback behavior are not explicit. The workflow boundary matters more than model novelty.

• A manual review queue has repetitive but judgment-sensitive work
• Teams cannot explain which data the generated output used
• Accuracy is discussed without task-specific acceptance criteria
• Low-confidence and exception cases have no controlled destination
• A prototype has no logging, feedback, fallback, or support owner

The engagement defines the task, source data, prohibited data, deterministic rules, generated output, reviewer decision, acceptance set, error handling, logging, and manual fallback before selecting a model or orchestration pattern.

• Workflow and decision boundary
• Data access, retention, privacy, and security limits
• Prompt, tool, retrieval, and deterministic-rule contract
• Evaluation set, thresholds, review, and exception handling
• Observability, feedback, fallback, release, and support path

Boundary

Task, data, reviewer, exception, and fallback definition

Prototype

Focused workflow slice with controlled inputs and outputs

Evaluation

Representative cases, deterministic checks, and review results

Operations

Logging, limitations, release, monitoring, and ownership notes

• 01 — Define the assistance task and the decision that remains human-owned.
• 02 — Establish data boundaries, prohibited behavior, and accepted examples.
• 03 — Build the smallest workflow with review and fallback included.
• 04 — Evaluate deterministic checks, quality, exceptions, and reviewer effort.
• 05 — Return the release, containment, expansion, or stop recommendation.

Public demonstrations use generalized or synthetic inputs and disclose the workflow boundary. Private prompts, records, customer data, credentials, model-provider configuration, and production outcomes remain outside the public surface.

• Users repeat classification, extraction, drafting, or summarization work
• The source data and prohibited data can be named
• A reviewer can approve, correct, reject, or escalate the output
• The team can provide representative examples and failure cases